SYSTEMS AND METHODS FOR GENERATING LANGUAGE MODEL CONTEXT USING REAL-TIME NETWORK INFORMATION AND DATA SOURCES

1 . A system, comprising:

one or more processors coupled to non-transitory memory, the one or more processors configured to:

maintain a vector database comprising an encoded set of wager opportunities corresponding to a plurality of live events;

receive, during a communication session from a client device, a prompt for a language model, the prompt identifying at least one request corresponding to a live event;

generate, using the prompt, a set of values corresponding to the vector database;

query the vector database using the set of values to retrieve at least one encoded wager of the encoded set of wagers;

generate an input context to provide to the language model based on the prompt and the at least one encoded wager;

generate, using the language model and the input context, an output message in response to the prompt; and

provide the output message to the client device in response to the prompt.

2 . The system of claim 1 , wherein the one or more processors are further configured to:

identify odds associated with the at least one encoded wager; and

update the output message generated by the language model to include the odds associated with the at least one encoded wager.

3 . The system of claim 1 , wherein the one or more processors are further configured to:

execute a vector search operation using at least a portion of the prompt to identify the at least one encoded wager.

4 . The system of claim 1 , wherein the one or more processors are further configured to:

identify a set of wagers corresponding to the plurality of live events; and

generate the encoded set of wagers using an embeddings model.

5 . The system of claim 1 , wherein the one or more processors are further configured to:

identify odds corresponding to the at least one encoded wager; and

modify the output message to include the odds corresponding to the at least one encoded wager.

6 . The system of claim 1 , wherein the one or more processors are further configured to:

maintain, in the vector database, an encoded set of event information corresponding to the plurality of live events;

retrieve, in response to a second prompt for the language model corresponding to a request for live event information, a subset of the encoded set of event information; and

generate a second output message using the language model and a second input context generated based on the second prompt and the subset of the encoded set of event information.

7 . The system of claim 1 , wherein the one or more processors are further configured to:

identify an additional wager corresponding to at least one of the plurality of live events; and

update the encoded set of wagers to include the additional wager in an encoded format in response to identifying the additional wager.

8 . The system of claim 1 , wherein the one or more processors are further configured to:

update the vector database according to an update schedule.

9 . The system of claim 1 , wherein the one or more processors are further configured to:

generate an encoded prompt using a tokenizer model and the prompt; and

generate the input context by combining the encoded prompt with the at least one encoded wager.

10 . The system of claim 1 , wherein the client device is associated with a player profile, and wherein the one or more processors are further configured to:

store a data structure identifying the communication session in association with the player profile, the data structure comprising the prompt and the output message.

11 . A method, comprising:

maintaining, by one or more processors coupled to non-transitory memory, a vector database comprising an encoded set of wager opportunities corresponding to a plurality of live events;

receiving, by the one or more processors during a communication session from a client device, a prompt for a language model, the prompt identifying at least one request corresponding to a live event;

generating, by the one or more processors using the prompt, a set of values corresponding to the vector database;

querying, by the one or more processors, the vector database using the set of values to retrieve at least one encoded wager of the encoded set of wagers;

generating, by the one or more processors, an input context to provide to the language model based on the prompt and the at least one encoded wager;

generating, by the one or more processors using the language model and the input context, an output message in response to the prompt; and

providing, by the one or more processors, the output message to the client device in response to the prompt.

12 . The method of claim 11 , further comprising:

identifying, by the one or more processors, odds associated with the at least one encoded wager; and

updating, by the one or more processors, the output message generated by the language model to include the odds associated with the at least one encoded wager.

13 . The method of claim 11 , wherein querying the vector database comprises:

executing, by the one or more processors, a vector search operation using at least a portion of the prompt to identify the at least one encoded wager.

14 . The method of claim 11 , further comprising:

identifying, by the one or more processors, a set of wagers corresponding to the plurality of live events; and

generating, by the one or more processors, the encoded set of wagers using an embeddings model.

15 . The method of claim 11 , further comprising:

identifying, by the one or more processors, odds corresponding to the at least one encoded wager; and

modifying, by the one or more processors, the output message to include the odds corresponding to the at least one encoded wager.

16 . The method of claim 11 , further comprising:

maintaining, by the one or more processors in the vector database, an encoded set of event information corresponding to the plurality of live events;

retrieving, by the one or more processors in response to a second prompt for the language model corresponding to a request for live event information, a subset of the encoded set of event information; and

generating, by the one or more processors, a second output message using the language model and a second input context generated based on the second prompt and the subset of the encoded set of event information.

17 . The method of claim 11 , further comprising:

identifying, by the one or more processors, an additional wager corresponding to at least one of the plurality of live events; and

updating, by the one or more processors, the encoded set of wagers to include the additional wager in an encoded format in response to identifying the additional wager.

18 . The method of claim 11 , further comprising:

updating, by the one or more processors, the vector database according to an update schedule.

19 . The method of claim 11 , further comprising:

generating, by the one or more processors, an encoded prompt using a tokenizer model and the prompt; and

generating, by the one or more processors, the input context by combining the encoded prompt with the at least one encoded wager.

20 . The method of claim 11 , wherein the client device is associated with a player profile, the method further comprising:

storing, by the one or more processors, a data structure identifying the communication session in association with the player profile, the data structure comprising the prompt and the output message.